1 👥 Cohort Description
Cohort Overview
| Characteristic | N = 1471 |
|---|---|
| Age | – |
| Median (Q1, Q3) | 37 (34, 44) |
| Unknown | 13 |
| Age group | – |
| age ≥15-<25 | 1/134 (0.7%) |
| age ≥25-<35 | 45/134 (34%) |
| age ≥35-<45 | 62/134 (46%) |
| age ≥45-<55 | 19/134 (14%) |
| age ≥55 | 7/134 (5.2%) |
| Unknown | 13 |
| Sex | – |
| Female | 38/134 (28%) |
| Male | 96/134 (72%) |
| Unknown | 13 |
| Period | – |
| First Three Days | 74/147 (50%) |
| Last Two Days | 73/147 (50%) |
| Years of Professional Experience | – |
| Median (Q1, Q3) | 11 (8, 17) |
| Unknown | 13 |
| Experience in VHF Response | 101/134 (75%) |
| Unknown | 13 |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) |
| Unknown | 26 |
| Envisioning Usage of IDMT in the Future | – |
| Yes | 121/121 (100%) |
| Unknown | 26 |
| 1 n/N (%) | |
Age and Sex Distribution
Cohort by Period
| Characteristic | Overall N = 1471 |
First Three Days N = 741 |
Last Two Days N = 731 |
p-value2 |
|---|---|---|---|---|
| Age | – | – | – | 0.67 |
| Median (Q1, Q3) | 37 (34, 44) | 37 (34, 43) | 37 (34, 44) | – |
| Unknown | 13 | 8 | 5 | – |
| Age group | – | – | – | 0.89 |
| age ≥15-<25 | 1/134 (0.7%) | 1/66 (1.5%) | 0/68 (0%) | – |
| age ≥25-<35 | 45/134 (34%) | 22/66 (33%) | 23/68 (34%) | – |
| age ≥35-<45 | 62/134 (46%) | 32/66 (48%) | 30/68 (44%) | – |
| age ≥45-<55 | 19/134 (14%) | 8/66 (12%) | 11/68 (16%) | – |
| age ≥55 | 7/134 (5.2%) | 3/66 (4.5%) | 4/68 (5.9%) | – |
| Unknown | 13 | 8 | 5 | – |
| Sex | – | – | – | 0.78 |
| Female | 38/134 (28%) | 18/66 (27%) | 20/68 (29%) | – |
| Male | 96/134 (72%) | 48/66 (73%) | 48/68 (71%) | – |
| Unknown | 13 | 8 | 5 | – |
| Years of Professional Experience | – | – | – | 0.66 |
| Median (Q1, Q3) | 11 (8, 17) | 11 (8, 17) | 12 (8, 18) | – |
| Unknown | 13 | 8 | 5 | – |
| Experience in VHF Response | 101/134 (75%) | 50/66 (76%) | 51/68 (75%) | 0.92 |
| Unknown | 13 | 8 | 5 | – |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) | 58/61 (95%) | 59/60 (98%) | 0.62 |
| Unknown | 26 | 13 | 13 | – |
| Envisioning Usage of IDMT in the Future | – | – | – | – |
| Yes | 121/121 (100%) | 61/61 (100%) | 60/60 (100%) | – |
| Unknown | 26 | 13 | 13 | – |
| 1 n/N (%) | ||||
| 2 Wilcoxon rank sum test; Fisher’s exact test; Pearson’s Chi-squared test | ||||
2 📊 Survey Overview
Overall, 96.7 % of participants considered IDTM an advantage to the systems used in the past and 100 are envisioning the usage of IDMT in the future (see Cohort Description above).
2.1 UEQ Scales Summary
| Category | Average1 | Std Dev. | Std Err. | 95% CI | 2 |
|---|---|---|---|---|---|
| Attractiveness | 2.337 | 0.867 | 0.079 | 2.18-2.49 | ▲ |
| Perspicuity | 1.853 | 1.078 | 0.098 | 1.66-2.05 | ▲ |
| Novelty | 1.463 | 1.106 | 0.101 | 1.26-1.66 | ▲ |
| Stimulation | 2.324 | 0.916 | 0.083 | 2.16-2.49 | ▲ |
| Dependability | 1.897 | 0.952 | 0.087 | 1.73-2.07 | ▲ |
| Efficiency | 2.132 | 0.942 | 0.086 | 1.96-2.3 | ▲ |
| 1 Values between -0.8and 0.8 represent a more or less neutral evaluation of the corresponding scale, values > 0,8 represent a positive evaluation and values < -0,8 represent a negative evaluation. | |||||
| 2 ▲ Positive Evaluation | ■︎ Neutral | ▼ Negative Evaluation) | |||||
2.2 Scales Distribution
Overall
By period
pd <- position_dodge(width = 0.9) # same width for bars and error bars
ggplot(summary_stats_period, aes(x = Scale, y = Mean, fill = period, group = period)) +
geom_bar(stat = "identity", color = "black", position = pd) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper),
position = pd, width = 0.2, color = "darkred", size = 1.5) +
# scale_fill_unhcr_d(direction = -1,) +
scale_fill_manual(values = c("#A7D3D4","#3182bd"))+
theme_unhcr() +
labs(
title = "UEQ Scale Means with 95% Confidence Intervals",
x = "Scale",
y = "Mean Score"
)
2.3 UEQ Mean values per items
2.4 Benchmark
The benchmark classifies a product into 5 categories (per scale):
Excellent: In the range of the 10% best results.
Good: 10% of the results in the benchmark data set are better and 75% of the results are worse.
Above average: 25% of the results in the benchmark are better than the result for the evaluated product, 50% of the results are worse.
Below average: 50% of the results in the benchmark are better than the result for the evaluated product, 25% of the results are worse.
Bad: In the range of the 25% worst results.
3 📊 Survey Results Distribution
Each item was rated on a 7-point scale from the left concept to the right. Lower values indicate stronger agreement with the first term (e.g., ‘Annoying’), while higher values reflect stronger agreement with the second (e.g., ‘Enjoyable’).
Attractiveness
Dependability
Efficiency
Novelty
Perspicuity
Stimulation
4 💹 Scale structure of the UEQ
5 ⏰ By Period
We used an ordinal logistic regression model to account for the ordered nature of the response scale. This allowed us to estimate the likelihood of each response level across time periods or experience in VHF response and visualize these shifts using the predicted probability plot.
5.1 📈 Attractiveness
Significance of Period Effect
Observed Response
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
5.2 📈 Novelty
Significance of Period Effect
Observed Response
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
5.3 📈 Perspicuity
Significance of Period Effect
Observed Response
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
5.4 📈 Stimulation
Significance of Period Effect
Observed Response
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
5.5 📈 Dependability
Significance of Period Effect
Observed Response
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
5.6 📈 Efficiency
Significance of Period Effect
Observed Response
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
6 👨🏽⚕️Experience in VHF Response
6.1 📈 Attractiveness
Significance of the Experience in VHF Response
Observed Response
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
6.2 📈 Novelty
Significance of the Experience in VHF Response
Observed Response
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
6.3 📈 Perspicuity
Significance of the Experience in VHF Response
Observed Response
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
6.4 📈 Stimulation
Significance of the Experience in VHF Response
Observed Response
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
6.5 📈 Dependability
Significance of the Experience in VHF Response
Observed Response
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
6.6 📈 Efficiency
Significance of the Experience in VHF Response
Observed Response
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
